In the medical industry, there is often a need for a laboratory technician, e.g., a cytotechnologist, to review a cytological specimen for the presence of specified cell types. For example, there is presently a need to review a cervical-vaginal Papanicolaou (Pap) smear slides. Pap smears have been a powerful tool for detecting cancerous and precancerous cervical lesions. The reliability and efficacy of a cervical screening and screening of other specimens is measured by its ability to diagnose precancerous lesions (sensitivity) while at the same time avoiding false positive diagnosis (specificity). In turn, these criteria depend on the accuracy of the cytological interpretation.
Traditionally, a pathologist may perform a single cell analysis on a biological specimen by looking at the characteristics of individual cell nuclei, or a contextual analysis on the biological specimen by looking for characteristic patterns in the architecture of the cells as they appear on the slide. To facilitate this review process, automated screening systems have been developed to process multiple microscope slides. In a typical system, an imager is operated to provide a series of images of a cytological specimen slide, each depicting a different portion of the slide. A processor or controller then processes the image data to furnish quantitative and prognostic information about the specimen. The processor can perform either a single cell analysis or a contextual analysis, or both, in providing this diagnostic information.
In some automated screening systems, the processor uses the diagnostic information to delineate between normal and abnormal or suspicious biological material within each specimen. That is, the processor will focus the cytotechnologist's attention on the most pertinent cells, with the potential to discard the remaining cells from further review. In this case, the screening device uses the diagnostic information to determine the most pertinent biological objects and their locations on the slide. This location information is provided to a review microscope, which automatically proceeds to the identified locations and centers on the biological objects for review by the cytotechnologist. The cytotechnologist can then electronically mark the most pertinent biological objects (for example, objects having attributes consistent with malignant or pre-malignant cells) for further review by a pathologist.
For example, in one automated system, objects or “objects of interest” (OOIs) are identified based on the image data. Objects or OOIs may take the form of individual cells and cell clusters of the specimen. The system may be configured to rank identified areas or objects, e.g., based on the degree to which certain cells or objects are at risk of having an abnormal condition such as malignancy or pre-malignancy. For example, a processor may evaluate objects for their nuclear integrated or average optical density, and rank the objects in accordance with their optical density values. The objects, along with their relative ranking and coordinates, may be stored for subsequent processing, review or analysis. Further aspects of a known imaging system and methods of processing image data and OOIs are described in U.S. Publication No. 2004/0254738 A1, the contents of which are incorporated herein by reference.
In general, the use of automated screening systems has been effective, since the technician's attention is focused on those slides that are suspicious or on a limited number of more pertinent objects within each slide. Automated screening systems, however, can be improved. For example, the manner in which automated systems process artifacts can be improved in order to reduce the rate of false positive or “false abnormal” results. An artifact may be considered to be an object which has no diagnostic value. One cause of false positives is the presence of artifacts, which may be abundant in a specimen sample and be in the form of large dark objects that mimic abnormal specimens. Artifacts may outrank objects containing normal cells.
For example, compared to an abnormal nucleus, a normal nucleus usually has less DNA amount and less texture. Without the presence of artifacts in the top ranked objects, the majority of the cells in a normal slide have tightly distributed DNA amounts. However, a large number of artifacts that mimic abnormal cells outrank the majority of the normal cells, and these artifacts create false alarms in data modeling. These artifacts may prevent true cells from being ranked and properly presented in the list of cells with the “top” DNA amounts. Thus, rather than selecting cells that should be reviewed, automated systems may instead mistakenly believe that an artifact is an abnormal cell and select artifacts that outrank an abnormal nucleus. This results in a selection of a smaller number of objects that actually have cells and selection of a smaller number of abnormal objects that warrant review by a cytotechnologist, thereby potentially resulting in less accurate and inaccurate analyses and diagnosis.
The occurrence of false positives sometimes results from the limited capabilities or configuration of an automated imager. That is, automated imagers may be limited by the specimen and data provided to them and by their programming. For example, for computational reasons, imagers typically use monochromatic, black and white images for their analyses. Examples of known monochromatic systems are available from Becton Dickinson Company, 1 Becton Drive, Franklin Lakes, N.J. and Cytyc Corporation, 250 Campus Drive, Marlborough, Mass. A specimen, however, may provide a great range of spectral data and other information that can be used to characterize or classify the sample. However, this other data is not available when using a monochromatic imaging and analysis system.